AI Full stack Developer
10 hours ago
Work Location: Chennai
Experience: 6-8 years
Education Qualification: Bachelor's Degree
Required Skills:
Technical Leadership
- Lead design, architecture, and development of AI-driven applications using GenAI and Agentic AI frameworks.
- Mentor and guide a team of developers, ensuring high-quality code and best practices.
- Collaborate with product managers, data scientists, and stakeholders to define technical roadmaps.
AI/ML Development
- Design, develop, and implement ML/DL models for predictive analytics, NLP, computer vision, and generative AI use cases.
- AI/ML Frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers
- Build and integrate LLMs, GenAI models, and agent-based AI workflows into enterprise solutions.
- Fine-tune, prompt-engineer, and optimize LLMs (OpenAI, Anthropic, Llama, etc.) for business use cases.
- Design and implement multi-agent orchestration systems for intelligent automation.
Fullstack Development
- Develop scalable web applications using / Angular / (frontend) and Python (Django / Flask / FastAPI / ) (backend).
- Integrate AI/ML APIs, microservices, and databases (SQL & NoSQL).
- Ensure security, scalability, and performance of deployed applications.
Cloud & MLOps
Monitor and optimize model performance in production.
Deploy and manage AI applications on AWS / Azure / GCP.
Implement CI/CD, containerization (Docker, Kubernetes), and model deployment pipelines.
- Define end-to-end architecture for GenAI & Agentic AI solutions
- Design scalable and secure AI pipelines including data ingestion, model training, fine-tuning, deployment, and monitoring.
- Architect multi-agent AI systems leveraging frameworks like LangChain, AutoGen, LlamaIndex, or similar.
Hands-on Development
- Build and optimize AI applications in Python using frameworks such as PyTorch, TensorFlow, Hugging Face Transformers.
- Implement and integrate LLMs, embeddings, RAG (Retrieval-Augmented Generation), vector databases (Pinecone, FAISS, Weaviate, Milvus).
- Develop autonomous AI agents for workflow automation, reasoning, and decision-making.
Innovation & Strategy
- Evaluate and recommend emerging GenAI & Agentic AI frameworks, tools, and platforms.
- Define best practices for prompt engineering, model fine-tuning, safety, compliance, and governance.
- Collaborate with stakeholders to align AI solutions with business goals and enterprise architecture.
Leadership & Mentoring
- Lead technical discussions and provide architectural guidance to engineering teams.
- Mentor data scientists, ML engineers, and software developers in AI best practices.
- Collaborate with product managers and business leaders on solution roadmaps.
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Tell me about a full-stack project where you deployed an AI feature end-to-end.
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How do you balance latency, throughput, accuracy, and cost in live AI features?
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